Ensembles in machine learning applications
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD...
Kaydedildi:
| Yazar: | |
|---|---|
| Diğer Yazarlar: | , , , |
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edisyon: | 1st ed. 2011. |
| Seri Bilgileri: | Studies in Computational Intelligence
373 |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Ensembles in Machine Learning Applications, Texte imprimé, 9783642229091 • Ensembles in Machine Learning Applications, Texte imprimé, 9783642229091 • Ensembles in Machine Learning Applications, Texte imprimé, 9783642229114 • Ensembles in Machine Learning Applications, Texte imprimé, 9783662507063 |
| LEADER | 04025nam a22004217a 4500 | ||
|---|---|---|---|
| 001 | 973335 | ||
| 008 | 111115q2000 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN156309882 | ||
| 020 | |a 9783642229107 | ||
| 041 | 0 | |a eng | |
| 082 | |a 006.3 | ||
| 100 | 1 | |a Okun, Oleg. | |
| 245 | 1 | 0 | |a Ensembles in machine learning applications |c edited by Oleg Okun, Giorgio Valentini, Matteo Re. |
| 250 | |a 1st ed. 2011. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 373 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a From the content: Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers On the Design of Low Redundancy Error-Correcting Output Codes Minimally-Sized Balanced Decomposition Schemes for Multi-Class Classification Bias-Variance Analysis of ECOC and Bagging Using Neural Nets Fast-ensembles of Minimum Redundancy Feature Selection | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications | ||
| 700 | 1 | |a Valentini, Giorgio. |4 edt | |
| 700 | 1 | |a Re, Matteo. |4 edt | |
| 700 | 1 | |a Okun, Oleg. |4 pbd | |
| 700 | 1 | |a Valentini, Giorgio, |d 19..- |4 pbd | |
| 700 | 1 | |a Re, Matteo, |d 19..- |4 pbd | |
| 776 | 0 | |t Ensembles in Machine Learning Applications |b Texte imprimé |z 9783642229091 | |
| 776 | 0 | |t Ensembles in Machine Learning Applications |b Texte imprimé |z 9783642229091 | |
| 776 | 0 | |t Ensembles in Machine Learning Applications |b Texte imprimé |z 9783642229114 | |
| 776 | 0 | |t Ensembles in Machine Learning Applications |b Texte imprimé |z 9783662507063 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-22910-7 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-HP78BX1M-1 |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:750613831 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-3-642-22910-7 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:753972638 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-3-642-22910-7 |z Accès INSA CVL | |
| 997 | |0 973335 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||